In [1]:
import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()
In [2]:
results_folder = 'mmvec_major_taxa_1'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'
In [3]:
table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()
Out[3]:
Propionibacteriaceae Staphylococcus caprae or capitis Staphylococcus epidermidis Staphylococcus hominis Other Staphylococci Polyomavirus HPyV6 Polyomavirus HPyV7 Merkel Cell Polyomavirus Malasseziaceae Corynebacteriaceae Micrococcaceae Other families
featureid
X940001 0.135536 0.061269 0.043672 0.044610 0.184495 0.037716 -0.020562 0.100228 0.186801 0.315386 0.153056 0.079198
X940002 -0.007746 -0.194599 -0.286544 -0.040674 -0.171444 -0.101813 -0.153303 -0.118492 -0.160680 -0.099536 -0.164102 -0.228855
X940005 -0.069363 -0.284181 -0.334273 -0.414055 -0.135930 0.357771 -0.014280 -0.047856 -0.396604 -0.174711 -0.159746 -0.241668
X940007 0.365357 0.282299 0.251389 0.551716 0.228546 0.003074 0.195018 0.249597 0.381006 0.334098 0.264940 0.242767
X940010 0.335508 0.006827 0.755825 0.447261 0.298167 0.484671 0.768211 0.686417 -0.164282 0.371776 0.572235 0.496835
In [4]:
#table['Selected'] = np.logical_and(np.logical_and(table['Propionibacteriaceae']<0.4, table['Staphylococcus epidermidis']>0.9), table['Propionibacteriaceae'] - table['Staphylococcus epidermidis']<-1)
table['Selected'] = np.isin(table.index,
                            ['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
                             'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
                             'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
                             'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
                             'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
                             'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
                             'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
                           )
table.sort_values('Selected', inplace=True)
sns.relplot(
    table,
    y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)
Out[4]:
<seaborn.axisgrid.FacetGrid at 0x7f2c34ef8790>
In [5]:
sns.pairplot(table, hue='Selected')
Out[5]:
<seaborn.axisgrid.PairGrid at 0x7f2c34cf0590>
In [6]:
for i in table.columns[:-1]:
    sns.displot(table, x=i, hue='Selected', multiple='stack')